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Course Introduction - Quality Control and Improvement with MINITAB.

Lecture 1: Introduction of Quality

Lecture 2: Voice of the Customer and Kano Model

Lecture 3: Quality Function Deployment

Lecture 4: Critical to Quality Characteristics

Lecture 5 : Data Visualization for Quality Control and Improvement

Lecture 6: Importance of Pareto Chart and Cause and Effect Diagram

Lecture 7: Design Failure Mode and Effect Analysis

Lecture 8: Introduction to Statistical Process Control

Lecture 9: X-bar and R Chart

Lecture 10: X-bar and S Chart

Lecture 11: Individual Moving Range Chart and Attribute Chart

Lecture 12: Attribute Control Charts and Process Capability

Lecture 13: Process Capability Index

Lecture 14: Process Performance and Sigma Level

Lecture 15: Process Capability for Attribute data

Lecture 16: Basic Statistics & Confidence Interval

Lecture 17: Hypothesis Testing

Lecture 18: One-sample t Test

Lecture 19: Two-sample t Test

Lecture 20: Paired t Test and ANOVA

Lecture 21: One-way ANOVA

Lecture 22: One-way ANOVA (Continued)

Lecture 23: ANCOVA and Nonparametric Test

Lecture 24: Linear Regression

Lecture 25: Linear Regression(Continued) and Multiple Regression

Lecture 26:Best Subset Regression, Multicollinearity

Lecture 27: Multicollinearity, Best Subset Regression, Multiple Regression...

Lecture 28: Design of Experiment, One-factor-at-a-time experiment

Lecture 29: Two-factor asymmetric Design, Symmetric Factorial Design, Two-way ANOVA

Lecture 30: Two-factor symmetric Design, Robust setting, Two-way ANOVA

Lecture 31: Measurement System Analysis

Lecture 32: Measurement System Analysis (Contd.)

Lecture 33: Measurement System Analysis (Contd.), Introduction to Factorial Experiments

Lecture 34: Factorial Experiments

Lecture 35: Factorial Experiments (Contd.)

Lecture 36: Factorial Experiments (Contd.)

Lecture 37: Blocking in Factorial Design.

Lecture 38: Multiple response Optimization & RSM

Lecture 39: Fractional Factorial Design

Lecture 40: Taguchi Method

Description:

This course will emphasize on application of different theories, tools, and techniques for Quality Control and Improvement. Most of the topics will be discussed with relevant problems and solutions in MINITAB 19 software interface.The course will emphasize two broad areas (e.g., Quality of Design and Quality of Conformance). In Quality of Design, relevant topics, such as VOC, Kano model, QFD, and FMEA, will be discussed with examples. Subsequently, the Quality of Conformance topics, such as quality control (e.g., statistical process control) and various topics related to process capability analysis, are discussed. With an objective to discuss topics related to the design of experiments, a few important statistical techniques, such as hypothesis testing, ANOVA, regression analysis, and MSA are covered in this course. Finally, various Design of Experiment (DOE) techniques for factor screening and quality improvement are elaborated with examples. These techniques include factorial designs, fractional factorial design, multiple response optimization, and the Taguchi method. Read more

Quality Control and Improvement with MINITAB

NPTEL

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#Business #Management & Leadership #Quality Management #Data Science #Data Visualization #Data Analysis #Minitab

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